Video navigation

ABSTRACT

A system and method for video navigation are disclosed. Motion analysis can be performed upon camera images to determine movement of a vehicle, and consequently present position of the vehicle. Feature points can be identified upon a video image. Movement of the feature points between video frames is indicative of movement of the vehicle. Video navigation can be used, for example, in those instances wherein GPS navigation is unavailable.

TECHNICAL FIELD

The present invention relates generally to navigation and, moreparticularly, to a method and system of video navigation that issuitable for use in unmanned aerial vehicles (UAV)s, for example.

BACKGROUND

UAVs are commonly used to provide surveillance in a variety ofapplications, ranging from battlefield surveillance to the monitoring ofhigh-value assets such as pipelines. UAVs often have an onboard videosystem that transmits a signal to a distant location, where the videosignal is monitored.

UAVs can utilize the Global Positioning System (GPS) as a primary meansof providing navigation. However, GPS signals are subject tointerference that can inhibit their use for navigation. Suchinterference can be either intentional or unintentional.

In the event of loss of the GPS signal, or other primary navigationmeans, a UAV can be programmed to return home. While it may be able toreturn home without the use of GPS (such as by using heading and lastknown location), it is likely that its mission will have been seriouslycompromised by the loss of ability to navigate.

As such, some means for providing backup navigation in the event of aloss of primary navigation is needed. In this manner, the UAV may beable to continue operations without substantial compromise. It isworthwhile to note that other contemporary means of navigation, such asinertial navigation, may not be suitable because of the limited payloadcapacity of small UAVs. Thus, it is desirable to provide a system forproviding the navigation of a small UAV which does not exceed thepayload capacity thereof.

SUMMARY

Methods and systems for video navigation are disclosed. For example,according to one or more embodiments of the present invention, a methodof video navigation can comprise performing motion analysis uponinformation from a video camera, such as a video camera that images theground. By combining the estimated ground motion information withknowledge of the aircraft attitude and the camera pointing angles, themotion of the UAV can be determined.

A set of equations can relate the position of a point on the groundbeing imaged to the position of the aircraft relative to some fixedcoordinate system, given knowledge of the camera pointing angle and theaircraft's attitude. Knowledge of the motion of the point on the groundcan then be used to determine the motion of the aircraft. This can bedone for a large number of points on the ground, and the resulteffectively averaged to provide a better solution.

Performing motion analysis upon information from a video camera cancomprise identifying a set of feature points in a video image,determining motion of the feature points by how they are mapped from afirst image to a second image of the video, and using the motion of thefeature points along with the aircraft and camera parameters todetermine the motion of a vehicle.

Identifying a set of feature points in a video image can comprise anumber of known techniques, including the use of corner detectors and/orthe SIFT operator. The present embodiment identifies points where thesum of image gradients in both horizontal and vertical directionsexceeds a threshold. The points can be selected from different portionsof the video image.

For example, the video image can be divided into a plurality ofdifferent cells and then a plurality of points can be selected from theplurality of cells. One point or more than one point can be selectedfrom each cell. The selected points can be those points that have thehighest gradient within the each cell. The gradient can be required toexceed a threshold value to assure that the point is suitable for use.Not all cells will necessarily contain at least one suitable point.

Different techniques RANSAC, optical flow, etc. can be used to determinethe correspondence between feature points in the first frame and featurepoints in the second frame. According to one embodiment of the presentinvention, an optical flow algorithm is used to determine how thefeature points are mapped from a first image to a second image. Forexample, an L-K optical flow algorithm with a multi-resolution pyramidcan be used.

Using motion of the feature points to determine motion of the UAVcomprises solving a set of equations to compute a motion model for theUAV. Various sets of suitable equations are known. Once the aircraftmotion model has been determined from the optical flow, the UAV motionmodel can then be used to determine the mapping of the feature pointsfrom the first image to the second image. Errors between the initialfeature point motion estimation based on optical flow, and the featurepoint estimation based on the aircraft motion model can then bedetermined.

Feature points where the errors are greater than a threshold areconsidered outliers and can be removed. The UAV motion model can then berecomputed. This outlier removal process can be iterated until theerrors for all remaining feature points are below a threshold.

During the period when GPS is providing a good navigation solution, thedifferences between the GPS navigation solution and the video navigationsolution can be determined. These errors can then be used to determinecorrections to be applied to the video navigation solution to increaseaccuracy, and reduce drift in the video navigation solution when a GPSsolution is not available.

Optionally, position fixes can be determined periodically, so as toenhance navigation accuracy. That is, such position fixes can be used tomitigate inaccuracies that accumulate during the use of videonavigation. Position fixes can be performed by comparing real-timeimages of the ground to stored reference images of ground, wherein thepositions of the stored reference images are known. In this manner, thelocation of the aircraft can be precisely determined periodically.

Further exemplary information regarding the use of such position fixescan be found in U.S. Pat. Nos. 5,809,171; 5,890,808; 5,982,930;5,982,945; and 5,946,422, for example, the contents of which are herebyexpressly incorporated by reference in their entirety.

The methods and system for video navigation disclosed herein areparticularly well suited for use with UAVs because UAVs typically haveonboard video systems, including cameras that are configured to imagethe ground with sufficient resolution. However, as those skilled in theart will appreciate, the methods and systems for video navigationdisclosed herein are also suitable for use in a variety of differentapplications.

One or more embodiments of video navigation of the present invention canthus be performed when other means of navigation are not functional. Inthis manner, a UAV can complete its mission after loss of primary GPSnavigation capability, for example. As the video camera is alreadyon-board the UAV, additional navigation payload is not required.

The scope of the invention is defined by the claims, which areincorporated into this section by reference. A more completeunderstanding of embodiments of the present invention will be affordedto those skilled in the art, as well as a realization of additionaladvantages thereof, by a consideration of the following detaileddescription of one or more embodiments. Reference will be made to theappended sheets of drawings that will first be described briefly.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a flow chart illustrating an overview of a method for videonavigation in accordance with an exemplary embodiment of the presentinvention;

FIG. 2 shows a flow chart illustrating the method of identifyinginteresting feature points in accordance with an exemplary embodiment ofthe present invention;

FIG. 3 shows an image of the ground illustrating a first scene, as usedin accordance with an exemplary embodiment of the present invention;

FIG. 4 shows an image of the ground illustrating a second scene as usedin accordance with an exemplary embodiment of the present invention, thesecond scene being at approximately the same location as the first sceneand being later in time (in this exemplary image, the second scene ismultiple frames after the first scene, so as to exaggerate the motion ofthe scenes for purposes of illustration);

FIG. 5 shows an image of the ground illustrating a plurality of motionvectors applied to the first scene of FIG. 3, according to an exemplaryembodiment of the present invention;

FIG. 6 shows a diagram illustrating a method of distance determinationthat takes into consideration differences in ground elevation, inaccordance with an exemplary embodiment of the present invention;

FIG. 7 shows a diagram illustrating the camera pointing angle, aircraftaltitude, and distance to the ground being imaged, in accordance with anexemplary embodiment of the present invention; and

FIG. 8 shows a block diagram illustrating a portion of a UAV havingvideo navigation according with an exemplary embodiment of the presentinvention.

Embodiments of the present invention and their advantages are bestunderstood by referring to the detailed description that follows. Itshould be appreciated that like reference numerals are used to identifylike elements illustrated in one or more of the figures.

DETAILED DESCRIPTION

According to one or more embodiments of the present invention, anavigation system for UAVs or other vehicles is provided. The navigationsystem can function as a backup navigation system. This navigationsystem can add very little, or no, size and weight to the UAV. Further,it has very little impact upon power consumption. Generally, most of thehardware required to implement this navigation will already be presenton a UAV. Thus, little or no hardware typically needs to be added.

Since one or more embodiments of the present invention adds little sizeand weight, the present invention can be suitable for use in small UAVs.However, one or more embodiments of the present invention can similarlybe suitable for use in larger UAVs and/or other airborne vehicles.

According to one or more embodiments of the present invention, backupnavigation is provided to a UAV in the event of loss of the primary GPSnavigation solution. The backup navigation can be based upon the use ofthe existing video data from the surveillance camera of the UAV. Asthose skilled in the art will appreciate, UAVs are commonly used forsome type of surveillance application and thus typically have a suitablevideo camera already installed.

Because the present invention can use a preexisting on-board videocamera and video processing circuitry, little or no additional hardwareis required. The backup navigation system of the present invention canacquire knowledge of the UAV's attitude and camera pointing angles fromUAV systems. This information is readily available.

The video navigation solution can be computed at the base stationoperations center and then be transmitted back to the UAV on the commandand control link. Calculation of the video navigation solution canalternatively be performed onboard the UAV.

The processor(s) used to compute the video navigation solution can beone or more general purpose processors, one or more custom processorssuch as application specific integrated circuits (ASICS), or anycombination of general purpose processors and custom processors. A smallprocessor or combination of processors is generally sufficient tocompute the video navigation solution. In any instance, the calculationof video navigation solution onboard the UAV results in a self-containedbackup navigation solution.

Generally, no modification to the existing video camera and relatedvideo processing equipment of a UAV is necessary. However, the videocamera and/or video processing equipment can be upgraded or otherwisemodified if desired.

Having a backup video navigation system according to one or moreembodiments of the present invention allows a UAV to continue itsmission, uninterrupted, when there is a loss of navigation informationfrom GPS. Advantageously, there may be little or no impact to either themission or UAV itself.

One aspect of video navigation, according to one or more embodiments ofthe present invention, is to perform motion analysis of the video outputso as to obtain information on position changes of key features betweentwo images in the video sequence. Either consecutive images (one imageimmediately follows the other, with no other images in between) can beused or non-consecutive images (other images are between the two images)can be used.

Information derived from the video images can be combined with aircraftand camera data, such as attitude, camera focal length, and camerapointing angle, to provide distance, heading, and altitude changeinformation. This information can then be used as the basis for a deadreckoning navigation system. Because this is a dead reckoning type ofnavigation system, there will typically be drifts in the navigationsolution over a period of time.

In order to correct for these drifts, an on-line calibration capabilityis provided. Errors between the primary GPS provided navigation solution(truth) and the video navigation solution are calculated. These errorsare used to estimate the bias errors in the navigation solution. A setof correction coefficients are then determined that can be applied tothe video navigation solution to decrease the drift when the primarynavigation system is off-line. It is likely that there will still belong term drifts in the video navigation solution.

In order to correct for these long term drifts, periodic position fixesmay be required. These position fixes can be obtained using a precisionimage registration algorithm (PIR). Such a precision image registrationalgorithm can compare an image from the video output of the UAV's camerawith map images stored in a database in order to determine the UAV'sposition more precisely. That is, images from the UAV's camera arecompared to images stored in a database, wherein the images stored inthe database have known locations. When a match is found, then thelocation of the UAV is also known. In this manner, present positioninformation in the navigation system can be updated periodically.

One advantage of the video navigation system of the present inventionwith respect to the sole use of a precision image registration algorithmis that the video navigation system of the present invention requiressubstantially less processing power. Thus, by primarily using the videonavigation system of the present invention for navigation and onlyperiodically using the precision image registration algorithm, therequired processing power is significantly reduced over that requiredfor application of just the precision image registration algorithm.

Given a description of the camera pointing angles relative to the UAV,the UAV's attitude, and the UAV's position relative to some fixedreference coordinate system, it is possible to write a set of equationsthat define the position of any pixel in an image frame relative to thesame fixed reference coordinate. As the UAV moves relative to thereference point, the apparent position of a point on the ground willchange position between different image frames of a video in a way thatis precisely predictable. By determining how key feature points movefrom one image to another in the video, it is possible to invert theprocess and determine how the UAV is moving. This is the basis for oneaspect of the video navigation system of the present invention.

FIG. 1 shows a flow chart illustrating an overview of an exemplaryembodiment of the present invention. According to this embodiment motionanalysis is performed upon information from a video camera of a UAV soas to determine movement, and consequently position, of the UAV overtime.

Inputs to the process are the video data or images 101, aircraftmetadata 104, and GPS navigation data 106. Aircraft metadata 104 cancomprise information regarding the aircraft attitude, the camerapointing angle, and the camera focal length (zoom).

At least one set of feature points is identified in video image 101, asindicated in block 102. Feature points are correlated between frames, asindicated in block 103. Correlating feature points between framescomprises determining which of the feature points present in one framecorrespond to feature points in another frame. That is, those featurepoints that are present from frame to frame are identified and tracked.

The motion of the feature points is determined according to how thesefeature points are mapped from one video image (an earlier video image)to another video image (a later video image), as indicated in block 103.There are a number of techniques to make the correspondence between thefeature points in the first image and the feature points in the secondimage. An L-K optical flow technique with multi-resolution pyramids canbe used in this embodiment of the invention.

The motion of the feature points can be used, along with the aircraftmetadata, to determine motion of the UAV, as indicated in block 107.There are often a large number of feature points available to use, and asmall number of aircraft motion parameters. This results in an overdetermined set of equations for the aircraft motion model, and a leastsquares technique can be used to solve the equations.

Interesting points are points that can possibly be used as featurepoints. They possess at least some desirable characteristics that makethem likely candidates for use as feature points. For example, they mayhave suitable gradients. Thus, interesting points are potential featurepoints. However, not all interesting points are necessarily used asfeature points.

It is not uncommon for the correspondences between interesting points intwo images to be inconsistent with the computed motion model of theaircraft, especially if an interesting point is part of an object(vehicle, person, etc.) that is moving independently from thebackground. This independent motion can substantially distort theaircraft motion model.

The potential problems caused by such independent motion can becorrected by computing the positions of the corresponding interestingpoints in the second image based on the positions of the interestingpoints in the first image and the computed motion model. The errorsbetween these calculated positions and the initial correspondences basedon the optical flow are then computed, as indicated in block 108.

An outlier removal process, indicated in block 109, can be used toremove the impact of these errors on the calculated solution, if theerrors are not below a threshold as determined in block 111. The toppercentages of points with correspondence errors that are above somedesired threshold are removed and the motion model parameters arerecomputed from the remaining correspondences. This process of removingoutliers can be continued until the errors are all below a desiredthreshold. This process allows independently moving objects to be in thevideo sequence without distorting the computation of the motion model.

There are a number of error sources that have an adverse impact upondead reckoning navigation. These errors sources can cause drift in thevideo navigation solution. One method for compensating for these errorsis to do an online calibration of the errors when the primary navigationsystem, e.g., GPS, is operating. During the time when the GPS isproducing a good navigation solution as determined in block 112, thevideo navigation system can also be running in the background. That is,video navigation corrections can be calculated, as indicated in block113.

When the GPS navigation solution is lost, the video navigation takesover, i.e., is applied as indicated in block 114 so as to provide videonavigation solution 115. The corrections can be used to remove the biaserror sources in the video navigation solution.

The use of the corrections to remove the system bias errors will reducethe drift from the dead reckoning navigation solution, but notcompletely eliminate it. The system may still require periodic positionfixes to continue to operate over a prolonged GPS outage. A precisionimage registration algorithm can be used to periodically provideposition fixes based on matching the current video image with an imagedatabase. Because the precision image registration algorithm tends to becomputationally intensive, it is only invoked periodically to providethe position fixes.

FIG. 2 shows a flow chart illustrating the method of selecting the setof feature points. The sum of the horizontal and vertical gradients iscomputed for each point in the image as indicated in block 201. Othertechniques, such as corner operators, SIFT points, etc. can also be usedto identify interesting feature points in an image.

Rather than having a large number of feature points developed in a smallarea of the image, a selection of interesting points from across theimage can be identified so as to enhance accuracy. Such a selection ofinteresting points can, for example, be obtained by dividing the imageinto a grid of cells as indicated in block 202. The point with thelargest gradient sum in each grid cell is chosen as a feature point inthat cell, as indicated in block 203. This chosen point is then comparedto a threshold as indicated in block 204, so as to determine if it isinteresting enough, e.g., the gradient is above the threshold, to use asa feature point in the motion calculation, as indicated in block 205. Ifthe gradient is below the threshold, then the point is not used as afeature point, as indicated in block 206. The use of such cells tends tospread processing across the image to take advantage of the land areacovered by the image in a manner that tends to enhance accuracy.

FIGS. 3-5 show an example of the image frame registration process andthe determination of a set of motion parameters describing the motion offrames in a video sequence. The registration process can be used todetermine motion of the feature points of the video images. An affinemotion model of the frames in the video sequence can be used todetermine the motion of the UAV from the image motion of the ground.

The images of FIGS. 3 and 4 are two frames from the same video sequence.The frames shown are multiple frames apart in order to exaggerate themotion for purposes of illustration. It is worthwhile to note that whenthe UAV moves, the camera on the UAV also moves, and generally, theentire image frame shifts, rotates, etc. So, it is not just the carsthat move in the video image. Everything in the video image typicallymoves. This movement is one aspect of the physical basis thatfacilitates video navigation according to one or more embodiments of thepresent invention.

In practice, such images can be consecutive images or can have one ormore intermediate images (can be multiple frames apart). Because theframes of FIGS. 3 and 4 are from the same camera, which is moving withthe UAV, and are multiple frames apart, the entire image scene hasmoved, and the same features in the different images are at differentlocations in the two different images.

FIG. 5 shows the image of FIG. 3 with motion vectors overlaid. Themotion of feature points within the image of FIG. 3 is in the directionof the motion vectors. Each vector can indicate movement of aninteresting or feature point in the sequence of video images.

All of the motion vectors in FIG. 5 are approximately the same directionand length. This is not necessarily true in general, but for this set ofimages it is true. It is worthwhile to note that there are no motionvectors attached to the independently moving objects (such as the cars),because any such motion vectors would not be consistent with the scenemotion and thus have been removed through the outlier removal process.

FIG. 7 shows a simplified diagram showing the relation between theaircraft position, the aircraft attitude, and the imaged point on theground. Shown are the camera pointing angles, the aircraft altitude, andthe distance from the aircraft to the imaged point on the ground.Initially, the aircraft is assumed to be flying generally straight andlevel over flat terrain. Given the x and y pixel coordinates in theimage, the corresponding x and y position on the ground in aircraftcoordinates can be found by a simple coordinate rotation based on thecamera pointing angles.

FIG. 7 illustrates a camera which has only two degrees of freedom forpointing, and would require only a two angle coordinate rotation. Othercamera pointing systems having three degrees of freedom for pointingwould require a three axis coordinate rotation. The result of thiscoordinate rotation determines the location of a point in the image inaircraft coordinates. As the aircraft is unlikely to be actually flyingstraight and level, another simple rotation based on the aircraftattitude parameters of pitch, roll, and yaw will provide the location ofan image point in earth coordinates relative to the nadir point of theaircraft. This relates the imaged feature point to the actual point onthe ground.

The change in position of a feature in the second image can then betranslated to a change in position of the aircraft. As many featurepoints are tracked, this results in an over determined set of equations.This set of equations can be solved using a least squares technique.

An assumption in these calculations is that the aircraft is at a lowenough altitude that we do not need to account for the spherical natureof the planet Earth. More complex equations can be identified in thecase where it is necessary to account for the spherical Earth. There areother sets of equations that can similarly be used to translate theground point position movement into UAV position.

The motion parameters are in units of pixels at this point, and need tobe converted to actual distances in order to provide the needednavigation information. The pixel size in meters can be determined byknowing the camera focal length and the distance from the camera to thepixel location. The distance can be determined by knowing the altitudeof the UAV above ground and the orientation of the camera to localhorizontal.

Changes in the terrain height also need to be accounted for in thedetermination of the distance to the pixel location. The elevation datafor the terrain can be determined from the Digital Elevation TerrainDatabase (DTED). FIG. 6, discussed in further detail below, is aschematic diagram showing the features in the distance determination.FIG. 6 is a schematic diagram showing the geometry involved indetermination of the distance from the UAV 81 to a point of interest 70in a video image of the ground 71. This distance needs to be correctedso as to properly account for the difference in altitude between thealtitude above ground 71 at the position of aircraft 81 and the altitudeof aircraft 81 above the point of interest 70. Differences in thesealtitudes can occur due to difference in the elevation of the ground atdifferent points. This correction accounts for the effective altitude atthe location of the camera pointing angle.

The process of determining the altitude at the camera pointing locationcan be an iterative process. The X and Y displacements are determined asdiscussed above for a given set of aircraft parameters assuming that thealtitude of the imaged ground point is the same as that of the aircraft.The altitude at the imaged point location can be looked up in theDigital Terrain Elevation Database (DTED) and the new altitude is usedto re-compute the X and Y offsets, which again are used to find a newaltitude. This is iterated until the change in altitude is below adesired threshold. Thus, the range to the ground 71 along the camerapointing direction can be determined. Along with the known focal lengthof the camera and other camera parameters, the distance represented by apixel can be determined.

FIG. 8 shows a block diagram illustrating a portion of UAV 81 havingvideo navigation equipment disposed therein, according to an exemplaryembodiment of the present invention. A window 82 formed in UAV 81facilitates imaging by video camera 83. Video camera 83 can be used forboth surveillance and video navigation.

Video camera 83 provides a video signal to processor 84. Processor 84can be either a general purpose processor or a custom processor.Processor 84 performs processing to facilitate surveillance according towell known principles and performs processing to facilitate videonavigation according to one or more embodiments of the presentinvention.

Optionally, additional processing can be provided. For example,additional processor 85 can perform processing functions that areunavailable in processor 84 or can merely augment processing byprocessor 84 (such as by performing processing functions that areavailable in processor 84).

All or a portion of the processing required for the practice of thepresent invention can be performed via a collocated (with respect tocamera 83) processor, such as processors 84 and 85. Alternatively, allor a portion of the processing can be performed via a remote processor,that is located some distance from camera 83. For example, the remoteprocessor can be located at the location for which the UAV is remotelycontrolled.

The navigation system of one or more embodiments of the presentinvention is discussed herein as being suitable for use in unmannedaerial vehicles (UAVs). However, the present invention is also suitablefor use in a variety of vehicles.

Various airborne vehicles, such as airplanes, helicopters, missiles, androckets can use one or more embodiments of the present inventionsubstantially as discussed herein. Thus, discussion herein of the use ofone or more embodiments of the present invention with UAVs is by way ofexample only, and not by way of limitation.

Embodiments described above illustrate but do not limit the invention.It should also be understood that numerous modifications and variationsare possible in accordance with the principles of the present invention.Accordingly, the scope of the invention is defined only by the followingclaims.

1. A method of navigation, the method comprising performing motionanalysis upon information from a video camera.
 2. The method of claim 1,wherein performing motion analysis upon information from a video cameracomprises analyzing position changes of key features between two imagesin a video sequence using vehicle and camera information.
 3. The methodof claim 1, wherein performing motion analysis upon information from avideo camera comprises analyzing position changes of key featuresbetween two images in a video sequence, and using vehicle attitude,camera focal length, and camera pointing angle to determine distance,heading, and altitude.
 4. The method of claim 1, wherein performingmotion analysis upon information from a video camera comprises:identifying a set of feature points in a video image; determining motionof the feature points by how they are mapped from a first image to asecond image; and using motion of the feature points to determine motionof a vehicle.
 5. The method as recited in claim 4, wherein identifying aset of feature points in a video image comprises identifying pointswhere a sum of image gradients in both horizontal and verticaldirections exceeds a threshold.
 6. The method as recited in claim 4,wherein identifying a set of feature points in a video image comprisesdividing the image into a plurality of cells and identifying a featurepoint in each cell.
 7. The method as recited in claim 4, whereinidentifying a set of feature points in a video image comprises dividingthe image into a grid of approximately 10 by approximately 15 cells andidentifying a plurality of points from a corresponding pluralitydifferent cells.
 8. The method as recited in claim 4, whereinidentifying a set of feature points in a video image comprises dividingthe image into a plurality of cells and identifying a point from eachcell.
 9. The method as recited in claim 4, wherein identifying a set offeature points in a video image comprises dividing the image into aplurality of cells and identifying a point from each cell, the pointbeing that point that has the highest gradient within the each cell whenthat gradient exceeds a threshold.
 10. The method as recited in claim 4,wherein determining how the feature points are mapped from a first imageto a second image comprises using an optical flow algorithm.
 11. Themethod as recited in claim 4, wherein determining how the feature pointsare mapped from a first image to a second image comprises using an L-Koptical flow algorithm with a multi-resolution pyramid.
 12. The methodas recited in claim 4, further comprising identifying feature pointsthat are moving with respect to a background of first video image bydetermining expected positions of the feature points on a second videoimage based upon position of the feature points in the first video imageand based upon predetermined motion of the vehicle.
 13. The method ofclaim 1, further comprising periodically determining position fixes. 14.The method of claim 1, further comprising periodically determiningposition fixes by comparing images of objects on the ground to objectsof stored images, wherein positions of the objects of the stored imagesknown.
 15. A method of navigation, the method comprising: performingmotion analysis upon information from a video camera; and wherein all ofthe motion analysis is performed via a processor that is collocated withrespect to the video camera.
 16. A method of navigation, the methodcomprising: performing motion analysis upon information from a videocamera; and wherein at least a portion of the motion analysis isperformed via a processor that is located remotely with respect to thevideo camera.